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基于3D點云數(shù)據(jù)的產品缺陷檢測研究

來源：電工電氣發(fā)布時間：2023-02-06 14:06 瀏覽次數(shù)：1151

基于3D點云數(shù)據(jù)的產品缺陷檢測研究

李潮林¹，陳仲生^1,2，左旺¹，侯幸林²

(1 湖南工業(yè)大學電氣與信息工程學院，湖南株洲 412007；

2 常州工學院汽車工程學院，江蘇常州 213032)

摘要：傳統(tǒng) 2D 視覺檢測技術存在效率低下、檢測精確度較低等不足，3D 視覺技術因能顯著提高缺陷檢測的效率和可靠性得到了高度關注和廣泛研究。對已有文獻進行了廣泛調研分析，介紹了 3D 點云數(shù)據(jù)的基本概念、獲取方式及其預處理方法，重點歸納了傳統(tǒng)點云數(shù)據(jù)缺陷檢測方法和點云數(shù)據(jù)深度學習缺陷檢測方法，并探討了當前研究中存在的問題與挑戰(zhàn)。

關鍵詞： 3D 視覺；缺陷檢測；點云數(shù)據(jù)

中圖分類號：TP391.41 文獻標識碼：A 文章編號：1007-3175(2023)01-0048-07

Research on Product Defect Detection Based on 3D Point Cloud Data

LI Chao-lin¹, CHEN Zhong-sheng^1,2, ZUO Wang¹, HOU Xing-lin²

（1 School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China;

2 School of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China）

Abstract: The traditional 2D vision detection technology has disadvantages of low efficiency and detection accuracy, while 3D vision technology can significantly improve its detection efficiency and reliability, so it has been paid high attention and widely analyzed. After making extensive analysis of the existing literature, the paper introduces the basic concept, access and pretreatment method of 3D point cloud data, summarizes the traditional point cloud data defect detection method and point cloud data deep learning defect detection method, and finally discusses the problems and challenges of the current research.

Key words: 3D vision; defect detection; point cloud data

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基于3D點云數(shù)據(jù)的產品缺陷檢測研究